arXiv:2603.05542v1 Announce Type: new Abstract: The rapid advancement of AI is transforming human-centered systems, with profound implications for human-AI interaction, human-data interaction, and visual analytics. In the AI era, data analysis increasingly involves large-scale, heterogeneous, and multimodal data that is predominantly unstructured, as well as foundation models such as LLMs and VLMs, which introduce additional uncertainty into analytical processes. These shifts expose persistent challenges for human-data interactive systems, including perceptually misaligned latency, scalability constraints, limitations of existing interaction […]

Ver mais

arXiv:2601.15290v1 Announce Type: new Abstract: Testing conversational AI systems at scale across diverse domains necessitates realistic and diverse user interactions capturing a wide array of behavioral patterns. We present a novel multi-agent framework for realistic, explainable human user simulation in interactive scenarios, using persona control and task state tracking to mirror human cognitive processes during goal-oriented conversations. Our system employs three specialized AI agents: (1) a User Agent to orchestrate the overall interaction, (2) a State Tracking Agent […]

Ver mais

arXiv:2510.08294v2 Announce Type: replace-cross Abstract: We address the open question of counterfactual identification for high-dimensional multivariate outcomes from observational data. Pearl (2000) argues that counterfactuals must be identifiable (i.e., recoverable from the observed data distribution) to justify causal claims. A recent line of work on counterfactual inference shows promising results but lacks identification, undermining the causal validity of its estimates. To address this, we establish a foundation for multivariate counterfactual identification using continuous-time flows, including non-Markovian settings under […]

Ver mais

We introduce the NeuroCore framework, a formal mathematical treatment of modular neural architectures in which a minimal executive Core—possessing no higher cognitive capabilities—autonomously orchestrates a heterogeneous collection of specialist modules through learned continuous-representation interfaces. The Core’s behavior is governed by two neuromodulation-inspired subsystems: a Dopamine System implementing distributional reinforcement learning with prediction-error intrinsic motivation and a stagnation penalty, and a Serotonin System formulated as a meta-reinforcement-learning controller that learns to optimize long-horizon constraint satisfaction. We make four theoretical […]

Ver mais

arXiv:2502.05730v3 Announce Type: replace-cross Abstract: Le Cam’s two-point testing method yields perhaps the simplest lower bound for estimating the mean of a distribution: roughly, if it is impossible to well-distinguish a distribution centered at $mu$ from the same distribution centered at $mu+Delta$, then it is impossible to estimate the mean by better than $Delta/2$. It is setting-dependent whether or not a nearly matching upper bound is attainable. We study the conditions under which the two-point testing lower bound […]

Ver mais

arXiv:2601.20968v1 Announce Type: new Abstract: In this paper we tackle the problem of adjusting roadmap graphs for robot motion planning to non-static environments. We introduce the “Red-Green-Gray” paradigm, a modification of the SPITE method, capable of classifying the validity status of nodes and edges using cheap heuristic checks, allowing fast semi-lazy roadmap updates. Given a roadmap, we use simple computational geometry methods to approximate the swept volumes of robots and perform lazy collision checks, and label a subset […]

Ver mais

Downstream adaptation of a contrastively pretrained vision–language model can improve in-domain accuracy while degrading performance on unseen transfer tasks. This study examines how full fine-tuning and low-rank adaptation alter attention heatmaps under a controlled design that matches learning rate across adaptation methods. The completed matched-learning-rate matrix contains 80 runs using the OpenAI Contrastive Language–Image Pretraining model with a base 32-patch vision transformer image encoder, two datasets (EuroSAT and Oxford-IIIT Pets), four shared learning rates (1e-6, 5e-6, 1e-5, and […]

Ver mais

arXiv:2602.10160v1 Announce Type: new Abstract: End-to-end autonomous driving systems have achieved significant progress, yet their adversarial robustness remains largely underexplored. In this work, we conduct a closed-loop evaluation of state-of-the-art autonomous driving agents under black-box adversarial threat models in CARLA. Specifically, we consider three representative attack vectors on the visual perception pipeline: (i) a physics-based blur attack induced by acoustic waves, (ii) an electromagnetic interference attack that distorts captured images, and (iii) a digital attack that adds ghost […]

Ver mais